Zinc finger-inspired peptide-metal-phenolic nanointerface enhances bone-implant integration under bacterial infection microenvironment through immune modulation and osteogenesis promotion
Why this work is in the frame
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Bibliographic record
Abstract
Orthopedic and dental implantations under bacterial infection microenvironment face significant challenges in achieving high-quality bone-implant integration. Designing implant coatings that incorporate both immune defense and anti-inflammation is difficult in conventional single-functional coatings. We introduce a multifunctional nanointerface using a zinc finger-inspired peptide-metal-phenolic nanocoating, designed to enhance implant osseointegration under such conditions. Abaloparatide (ABL), a second-generation anabolic drug for treating osteoporosis, can be integrated into the design of a zinc-phenolic network constructed on the implant surface (ABL@ZnTA). Importantly, the phenolic-coordinated Zn2+ ions in ABL@ZnTA can act as zinc finger motif to co-stabilize the configuration of ABL through multiple molecular interactions, enabling high bioactivity, high loading capacity (1.36 times), and long-term release (>7 days) of ABL. Our results showed that ABL@ZnTA can modulate macrophage polarization from the pro-inflammatory M1 towards the anti-inflammatory M2 phenotype, promoting immune osteogenesis with increased OCN, ALP, and SOD 1 expression. Furthermore, the ABL@ZnTA significantly reduces inflammatory fibrous tissue encapsulation and enhances the long-term stability of the implants, indicated by enhanced binding strength (6 times) and functional connectivity (1.5−3 times) in the rat bone defect model infected by S. aureus. Overall, our research offers a nano-enabled synergistic strategy that balances infection defense and osteogenesis promotion in orthopedic and dental implantations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it